The dqrng package provides several fast random number
generators together with fast functions for generating random numbers
according to a uniform, normal and exponential distribution. These
functions are modeled after the base functions
set.seed, RNGkind, runif,
rnorm, and rexp.
dqrrademacher uses a fast algorithm to generate random
Rademacher variables (-1 and 1 with equal probability). To do so, it
generates a random 64 bit integer and then uses each bit to generate
a 0/1 variable. This generates 64 integers per random number generation.
dqRNGkind(kind, normal_kind = "ignored")
dqrunif(n, min = 0, max = 1)
dqrnorm(n, mean = 0, sd = 1)
dqrexp(n, rate = 1)
dqrrademacher(n)
dqset.seed(seed, stream = NULL)string specifying the RNG (see details)
ignored; included for compatibility with RNGkind
number of observations
lower limit of the uniform distribution
upper limit of the uniform distribution
mean value of the normal distribution
standard deviation of the normal distribution
rate of the exponential distribution
integer scalar to seed the random number generator, or an integer vector of length 2 representing a 64-bit seed. Maybe NULL, see details.
integer used for selecting the RNG stream; either a scalar or a vector of length 2
dqrunif, dqrnorm, and dqrexp return a numeric vector of length n. dqrrademacher returns an integer vector of length n.
Supported RNG kinds:
The default 64 bit variant from the PCG family developed by Melissa O'Neill. See https://www.pcg-random.org/ for more details.
RNGs developed by David Blackman and Sebastiano Vigna. See https://xoshiro.di.unimi.it/ for more details. The older generators Xoroshiro128+ and Xoshiro256+ should be used only for backwards compatibility.
The 64 bit version of the 20 rounds Threefry engine as
provided by sitmo-package
Xoroshiro128++ is the default since it is fast, small and has good statistical properties.
The functions dqrnorm and dqrexp use the Ziggurat algorithm as
provided by boost.random.
See generateSeedVectors for rapid generation of integer-vector
seeds that provide 64 bits of entropy. These allow full exploration of
the state space of the 64-bit RNGs provided in this package.
If the provided seed is NULL, a seed is generated from R's RNG
without state alteration.
library(dqrng)
# Set custom RNG.
dqRNGkind("Xoshiro256++")
# Use an integer scalar to set a seed.
dqset.seed(42)
# Use integer scalars to set a seed and the stream.
dqset.seed(42, 123)
# Use an integer vector to set a seed.
dqset.seed(c(31311L, 24123423L))
# Use an integer vector to set a seed and a scalar to select the stream.
dqset.seed(c(31311L, 24123423L), 123)
# Random sampling from distributions.
dqrunif(5, min = 2, max = 10)
#> [1] 6.671009 6.819399 5.809120 7.420207 9.432123
dqrexp(5, rate = 4)
#> [1] 0.3195102 0.1255143 0.3573952 0.7278543 0.1367707
dqrnorm(5, mean = 5, sd = 3)
#> [1] 2.479324 2.667785 4.365715 7.538162 4.144456